Discrete Bayesian Sample Inference for Graph Generation

ArXi:2511.03015v2 Announce Type: replace Generating graph-structured data is crucial in applications such as molecular generation, knowledge graphs, and network analysis. However, their discrete, unordered nature makes them difficult for traditional generative models, leading to the rise of discrete diffusion and flow matching models. In this work, we